Research Publications Affiliated with Luxembourg: A Data-Driven Analysis

1 Introduction

This report presents a comprehensive analysis of Luxembourg’s research publication landscape using data extracted from OpenAlex, an open-access scholarly metadata platform. OpenAlex serves as a freely accessible alternative to proprietary academic databases, providing structured information about publications, authors, institutional affiliations, and research collaborations across all academic disciplines.

The data collection methodology focuses on identifying all scholarly works with Luxembourg institutional affiliations recorded in OpenAlex over the past decade. This approach captures both research led by Luxembourg-based scholars and international collaborative projects where Luxembourg institutions participate as co-authors. The temporal scope provides a current snapshot of the country’s scientific output and evolving research partnerships.

OpenAlex aggregates metadata from multiple sources including institutional repositories, publisher databases, and citation networks. While this multi-source approach enhances coverage comprehensiveness, data quality depends on the accuracy of source reporting and the platform’s ability to correctly identify and link Luxembourg-affiliated works. These methodological considerations should be kept in mind when interpreting the analytical findings presented throughout this report.

2 Data Structure and Document Types

The initial dataset encompasses all document types recorded in OpenAlex for Luxembourg-affiliated scholarly works during the study period. The following table displays the distribution of work types and the presence of Digital Object Identifiers (DOIs), which serve as persistent identifiers linking to original publications:

Table 1: Distribution of types of work and missingness of DOI

Based on the substantial predominance of journal articles in the dataset and their central importance in academic research communication, this analysis restricts its focus to articles exclusively. This selection encompasses both publications with DOI identifiers and those without, ensuring comprehensive coverage of Luxembourg’s peer-reviewed research output.

The analytical framework employs a critical distinction based on first authorship status: whether the primary author maintains affiliation with a Luxembourg institution or represents an international collaboration where Luxembourg institutions participate as secondary contributors. This classification enables differentiation between research leadership and research participation within the national research ecosystem:

Table 2: Number of articles where the first author is LU-affiliated

Select Year Range:

3 Research Domain Analysis

The following visualization examines the temporal distribution of Luxembourg-affiliated research across major scientific domains. The data utilizes OpenAlex’s domain classification system, which categorizes research fields into broad disciplinary areas. The analysis tracks publication volumes over time while maintaining the distinction between Luxembourg-led research (first author affiliation) and collaborative research (non-first author participation):

Observable js version of the graph:

ojs_define(primary_domain_lu_ojs = primary_domain_lu)

import {Plot} from "@observablehq/plot"
import {Inputs} from "@observablehq/inputs"
import {rangeInput} from "@mootari/range-slider"


// Convert R data to JavaScript format
raw_data = transpose(primary_domain_lu_ojs)

// Convert data types to ensure Observable Plot can use them
data = raw_data.map(d => ({
  publication_year: +d.publication_year,  // Convert to number
  primary_domain_name: d.primary_domain_name,
  total: +d.total,  // Convert to number
  is_lu_first_author: d.is_lu_first_author === "TRUE" || d.is_lu_first_author === true  // Convert to boolean
}))

// Create color mapping
domain_colors = new Map([
  ["Health Sciences", "#FF6B35"],
  ["Life Sciences", "#003399"],
  ["MISSING-DOMAIN", "#228B22"],
  ["Physical Sciences", "#FF1493"],
  ["Social Sciences", "#800080"]
])

// Get unique values for controls
unique_years = [...new Set(data.map(d => d.publication_year))].sort()
unique_domains = [...new Set(data.map(d => d.primary_domain_name))].sort()

viewof year_range = rangeInput({
  min: 2015,
  max: 2025,
  value: [2015, 2025],
  title: 'Select year interval',
  step: 1
})

viewof selected_domains = Inputs.checkbox(
  unique_domains,
  {
    value: unique_domains,
    label: "Research Domains"
  }
)

viewof selected_author_type = Inputs.radio(
  ["LU-affiliated first author", "Non LU-affiliated first author", "Both"],
  {
    value: "Both",
    label: "First Author Affiliation"
  }
)


// Filter data based on selections
filtered_data = data.filter(d => {
  // Year filter
  const yearOk = d.publication_year >= year_range[0] && d.publication_year <= year_range[1];
  
  // Domain filter
  const domainOk = selected_domains.includes(d.primary_domain_name);
  
  // Author filter
  let authorOk = false;
  if (selected_author_type === "Both") {
    authorOk = true;
  } else if (selected_author_type === "LU-affiliated first author") {
    authorOk = d.is_lu_first_author === true;
  } else if (selected_author_type === "Non LU-affiliated first author") {
    authorOk = d.is_lu_first_author === false;
  }
  
  return yearOk && domainOk && authorOk;
})


// Create the plot
//| echo: false

// Create the plot with better visual distinction
// Create the plot
Plot.plot({
  width: 800,
  height: 500,
  marginLeft: 60,
  marginBottom: 60,
  x: {
    label: "Publication Year",
    domain: [Math.min(...unique_years), Math.max(...unique_years)],
    tickFormat: "d"
  },
  y: {
    label: "Number of Publications",
    grid: true
  },
  color: {
    domain: unique_domains,
    range: unique_domains.map(d => domain_colors.get(d)),
    legend: true
  },
  marks: [
    // Solid lines for LU-affiliated authors
    Plot.line(filtered_data.filter(d => d.is_lu_first_author === true), {
      x: "publication_year",
      y: "total",
      stroke: "primary_domain_name",
      strokeWidth: 2.5,
      z: d => `${d.primary_domain_name}-LU`
    }),
    // Dashed lines for non-LU-affiliated authors
    Plot.line(filtered_data.filter(d => d.is_lu_first_author === false), {
      x: "publication_year",
      y: "total",
      stroke: "primary_domain_name",
      strokeWidth: 2.5,
      strokeDasharray: "8,8",
      z: d => `${d.primary_domain_name}-NonLU`
    }),
    // Points with different symbols
    Plot.dot(filtered_data, {
      x: "publication_year",
      y: "total",
      fill: "primary_domain_name",
      symbol: d => d.is_lu_first_author ? "circle" : "triangle",
      r: 5,
      stroke: "white",
      strokeWidth: 1
    }),
    // Tooltips
    Plot.tip(filtered_data, Plot.pointer({
      x: "publication_year",
      y: "total",
      fill: "primary_domain_name",
      title: d => `${d.primary_domain_name}\n${d.publication_year}\nPublications: ${d.total}\nLU First Author: ${d.is_lu_first_author ? "Yes" : "No"}`
    }))
  ],
  title: "Luxembourg Research Publications by Domain Over Time"
})

html`
<div style="margin-top: 20px; padding: 15px; border: 1px solid #ddd; border-radius: 5px; background-color: #f9f9f9;">
  <h4 style="margin-top: 0; margin-bottom: 10px;">Author Type Legend:</h4>
  <div style="display: flex; gap: 30px; align-items: center;">
    <div style="display: flex; align-items: center; gap: 8px;">
      <svg width="30" height="20">
        <line x1="0" y1="10" x2="30" y2="10" stroke="#333" stroke-width="2.5" />
        <circle cx="15" cy="10" r="4" fill="#333" stroke="white" stroke-width="1"/>
      </svg>
      <span><strong>LU-affiliated first author</strong> (solid line + circle)</span>
    </div>
    <div style="display: flex; align-items: center; gap: 8px;">
      <svg width="30" height="20">
        <line x1="0" y1="10" x2="30" y2="10" stroke="#333" stroke-width="2.5" stroke-dasharray="2,8"/>
        <polygon points="15,6 19,14 11,14" fill="#333" stroke="white" stroke-width="1"/>
      </svg>
      <span><strong>Non-LU-affiliated first author</strong> (dashed line + triangle)</span>
    </div>
  </div>
</div>
`

4 International Collaboration Patterns

The analysis extends to examining Luxembourg’s research collaboration patterns with international partners. The dataset contains information about co-author affiliations, enabling identification of the most frequent collaborating countries and regions. This section presents the geographical distribution of research partnerships, organized by publication year and distinguished by Luxembourg’s role as lead author versus collaborative partner.

The data processing groups countries into meaningful categories, including major individual nations, regional blocs, and an aggregated “Others” category for countries with lower collaboration frequencies. This approach provides clarity while maintaining analytical depth regarding Luxembourg’s primary research partnerships: